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NVIDIA takes on the supercomputing market with Tesla

NVIDIA intensifies its push into the supercomputing market with the launch of …

When rumors cropped up in early April that NVIDIA would be launching a separate GPU line and brand targeted specifically at high-performance computing (HPC), I was sure they were true. It only made sense that NVIDIA, which has been very clear about how serious they are about HPC, would take their efforts to emphasize the non-graphics applications of their G80 line to the next level by doing a full-scale rebranding push.

Yesterday, NVIDIA went public with this next phase of their HPC plans by launching the rumored Tesla line of HPC-oriented GPUs. The basis of the line is the Tesla C870 GPU, which appears to be a version of the workstation-class QuadroFX 5600 without the discrete display chip. The next rung up on the Tesla ladder is the D870 Deskside Supercomputer, an enclosure that houses two C870's and plugs into a PCIe slot via a host adapter card. At the top of the line is the S870 GPU Computing Server. The S780 is a 1U rackmount enclosure with four D870s packed inside. That's a lot of GPU compute power, and at 550W it's also a lot of electrical power. When NVIDIA's high-end GPU line moves down to a more advanced process node, that's going to help a lot with the power consumption.

The Tesla line isn't really ready for HPC primetime, lacking as it does the capability to do double-precision floating-point. This capability is coming in the next version of the product, however, so potential customers won't have to wait long. And again, the GPUs need to move down a process node to at least 65nm before their appeal widens. The coming combination of lower-power and double-precision floating-point will make the Tesla line quite potent a the fairly specific range of data-parallel workloads.

Incidentally, I was across town from the NVIDIA launch at Research@Intel day, where one of the Intel researchers told me that the main challenge to doing fully IEEE-compliant double-precision floating-point on GPUs is handling exceptions. I should've asked him more about this, because it's not intuitively obvious to me why this should be the case. But I'd love to hear from readers who know more about it than I do.